1. Field of the Invention
This invention relates generally to spectroscopic data processing data technology and its application in calibration and noninvasive measurement of blood analytes, such as glucose. More particularly, this invention relates to a method for attenuating spectroscopic interference resulting from tissue heterogeneity, patient-to-patient variation, instrument related variation, and physiological variation.
2. Background Information
The need for an accurate, noninvasive method for measuring blood analytes, particularly glucose is well understood and documented. Diabetes is a leading cause of death and disability worldwide and afflicts an estimated 16 million Americans. Complications of diabetes include heart and kidney disease, blindness, nerve damage and, high blood pressure with the estimated total cost to United States economy alone exceeding $90 billion per year. Diabetes Statistics, Publication No. 98-3926, National Institutes of Health, Bethesda Md. (November 1997). Long-term clinical studies show that the onset of complications can be significantly reduced through proper control of blood glucose levels. The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N Eng J of Med, 329:977–86 (1993). A vital element of diabetes management is the self-monitoring of blood glucose levels by diabetics in the home environment. A significant disadvantage of current monitoring techniques is that they discourage regular use due to the inconvenience and pain involved in drawing blood through the skin prior to analysis. Therefore, new methods for self-monitoring of blood glucose levels are required to improve the prospects for more rigorous control of blood glucose in diabetic patients.
Numerous approaches have been explored for measuring blood glucose levels, ranging from invasive methods such as microdialysis to noninvasive technologies that rely on spectroscopy. Each method has associated advantages and disadvantages, but only a few have received approval from certifying agencies. To date, no noninvasive techniques for the self-monitoring of blood glucose have been certified.
One method using near-infrared spectroscopy involves the illumination of a spot on the body with near-infrared electromagnetic radiation which is light in the wavelength range 700 to 2500 nm. The light is partially absorbed and scattered, according to its interaction with the tissue constituents prior to being reflected back to a detector. The detected light contains quantitative information that is based on the known interaction of the incident light with components of the body tissue including water, fat, protein, and glucose.
Previously reported methods for the noninvasive measurement of glucose through near-infrared spectroscopy rely on the detection of the magnitude of light attenuation caused by the absorption signature of blood glucose as represented in the targeted tissue volume. The tissue volume is the portion of irradiated tissue from which light is reflected or transmitted to the spectrometer detection system. The spectroscopic signal related to glucose is extracted from the spectral measurement through various methods of signal processing and one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary set of spectral measurements and associated reference blood glucose values (the calibration set) based on an analysis of capillary (fingertip), alternative invasive, or venous blood.
Near-infrared spectroscopy has been demonstrated in specific studies to represent a feasible and promising approach to the noninvasive prediction of blood glucose levels. One of the studies reports three different instrument configurations for measuring diffuse transmittance through the finger in the 600–1300 nm range. Meal tolerance tests were used to perturb the glucose levels of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation. Absolute average prediction errors ranged from 19.8 to 37.8 mg/dL. M. Robinson, R. Eaton, D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasive glucose monitoring in diabetic patients: A preliminary evaluation, Clin Chem, 38:1618–22 (1992).
Other studies present results through a diffuse reflectance measurement of the oral mucosa in the 1111–1835 nm range with an optimized diffuse reflectance accessory. In vivo experiments were conducted on single diabetics using glucose tolerance tests and on a population of 133 different subjects. The best standard error of prediction reported was 43 mg/dL and was obtained from a two-day single person oral glucose tolerance test that was evaluated through cross-validation. H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasive blood glucose sensors based on near-infrared spectroscopy, Artif Org, 18:439–47 (1994); H. Heise, R. Marbach, Effect of data pretreatment on the noninvasive blood glucose measurement by diffuse reflectance near-IR spectroscopy, SPIE Proc, 2089:114–5 (1994); R. Marbach, T. Koschinsky, F. Gries, H. Heise, Noninvasive glucose assay by near-infrared diffuse reflectance spectroscopy of the human inner lip, Appl Spectrosc, 47:875–81 (1993) and R. Marbach, H. Heise, Optical diffuse reflectance accessory for measurements of skin tissue by near-infrared spectroscopy, Applied Optics 34(4):610–21 (1995).
Some other studies have recorded spectra in diffuse reflectance over the 800–1350 nm range on the middle finger of the right hand with a fiber-optic probe. Each experiment involved a diabetic subject and was conducted over a single day with perturbation of blood glucose levels through carbohydrate loading. Results, using both partial least squares regression and radial basis function neural networks were evaluated on single subjects over single days through cross-validation. An average root mean square prediction error of 36 mg/dL through cross-validation over 31 glucose profiles has also been reported. K. Jagemann, C. Fischbacker, K. Danzer, U. Muller, B. Mertes, Application of near-infrared spectroscopy for noninvasive determination of blood/tissue glucose using neural network, Z Phys Chem, 191S:179–190 (1995); C. Fischbacker, K. Jagemann, K. Danzer, U. Muller, L. Papenkrodt, J. Schuler, Enhancing calibration models for noninvasive near-infrared spectroscopic blood glucose determinations, Fresenius J Anal Chem 359:78–82 (1997); K. Danzer, C. Fischbacker, K. Jagemann, K. Reichelt, Near-infrared diffuse reflection spectroscopy for noninvasive blood-glucose monitoring, LEOS Newsletter 12(2):9–11 (1998); and U. Muller, B. Mertes, C. Fischbacker, K. Jagemann, K. Danzer, Noninvasive blood glucose monitoring by means of new infrared spectroscopic methods for improving the reliability of the calibration models, Int J Artif Organs, 20:285–290 (1997).
In a study of five diabetic subjects conducted over a 39-day period with five samples taken per day, absorbance spectra through a transmission measurement of the tongue in the 1429–2000 nm range were collected. Every fifth sample was used for an independent test set and the standard error of prediction for all subjects was greater than 54 mg/dL. J. Burmeister, M. Arnold, G. Small, Human noninvasive measurement of glucose using near infrared spectroscopy (abstract), Pittcon, New Orleans La. (1998).
In a study involved in noninvasive measurement of blood glucose during modified oral glucose tolerance tests over a short time period, the calibration was customized for the individual and tested over a relatively short time period. T. Blank, T. Ruchti, S. Malin, S. Monfre, The use of near-infrared diffuse reflectance for the noninvasive prediction of blood glucose, IEEE Lasers and Electro-Optics Society Newsletter,13:5 (October 1999).
In all of these studies, limitations are cited that would affect the acceptance of such a method as a commercial product. These limitations include sensitivity, sampling problems, time lag, calibration bias, long-term reproducibility, and instrument noise. Fundamentally, however, accurate noninvasive estimation of blood glucose is presently limited by the available near-infrared technology, the trace concentration of glucose relative to other constituents, and the complex nature of the skin and living tissue of the patient. O. Khalil, Spectroscopic and clinical aspects of noninvasive glucose measurements, Clin Chem, 45:165–77 (1999).
As we have discovered, chemical, structural, and physiological variations occur that produce dramatic and nonlinear changes in the optical properties of the tissue sample. S. Malin, T. Ruchti, An Intelligent System for Noninvasive Blood Analyte Prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001). Relevant studies may be found in the following references: R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, 7:1, pp.13–19 (1981), W. Cheong, S. Prahl, A. Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, 26:12, pp.2166–2185, (December 1990), D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue using time-resolved spectrophotometry: the impact of statically and dynamically variable optical path lengths, SPIE, 1888, pp.10–21 (1993), J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, 40, pp.1123–1140 (December 1984), S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, 1:4, pp.418–424 (October 1996), A. Profio, Light transport in tissue, Applied Optics, 28:12), pp. 2216–2222, (June 1989), M. Van Gemert, S. Jacques, H. Sterenborg, W. Star, Skin optics, IEEE Transactions on Biomedical Engineering, 36:12, pp.1146–1154 (December 1989), and B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, 26:12, pp. 2186–2199.
In particular, the characteristics and variation of the tissue sample produce profound interference in the tissue measurement that leads to degradation in the accuracy and precision noninvasive glucose measurements. For example, the near-infrared diffuse reflectance (absorbance) spectrum is a complex combination of the tissue scattering properties that are dominated by the concentration and characteristics of a multiplicity of tissue components including water, fat, protein, and glucose. Physiological variation causes dramatic changes in the tissue measurement over time and lifestyle, health, aging, and environmental exposure lead to spectrally manifested structural variations. Errors in glucose measurements develop when the net analyte signal of glucose is attenuated by interference or when the sample is outside the effective range of the calibration model.
The measurement is further complicated by the heterogeneity of the sample, the multi-layered structure of the skin, changes in the volume fraction of blood in the tissue, hormonal stimulation, temperature fluctuations, and blood analyte levels. This can be further considered through a discussion of the properties of skin.
Tissue Scattering Properties
1. Skin Structure
The structure and composition of skin varies widely among individuals, between different sites within an individual, and over time on the same individual. Skin includes a superficial layer known as the stratum corneum, a stratified cellular epidermis, and an underlying dermis of connective tissue. Below the dermis is the subcutaneous fatty layer or adipose tissue. The epidermis, with a thickness of 10–150 μm, together with the stratum corneum provides a barrier to infection and loss of moisture and other body constituents, while the dermis is the thick inner layer that provides mechanical strength and elasticity. F. Ebling, The Normal Skin, Textbook of Dermatology, 2nd ed.; A. Rook; D. Wilkinson, F. Ebling, Eds.; Blackwell Scientific, Oxford, pp 4–24 (1972). In humans, the thickness of the dermis ranges from 0.5 mm over the eyelid to 4 mm on the back and averages approximately 1.2 mm over most of the body. S. Wilson, V. Spence, Phys. Med. Biol., 33:894–897 (1988).
In the dermis, water accounts for approximately 70% of the volume. The next most abundant constituent is collagen, a fibrous protein comprising 70–75% of the dry weight of the dermis. Elastin fibers, also a protein, are plentiful though they constitute a smaller proportion of the bulk. In addition, the dermis contains a wide variety of structures (e.g., sweat glands, hair follicles, and blood vessels) and other cellular constituents. F. Ebling, supra. Conversely, the subcutaneous layer (adipose tissue) is by volume approximately 10% water and is composed primarily of cells rich in triglycerides or fat. The concentration of glucose varies in each layer according to a variety of factors which include the water content, the relative sizes of the fluid compartments, the distribution of capillaries, the perfusion of blood, the glucose uptake of cells, the concentration of glucose in blood, and the driving forces (e.g. osmotic pressure) behind diffusion. Due to the high concentration of fat, the average concentration of water soluble glucose in subcutaneous tissue is significantly lower than that of the dermis.
2. Skin Properties
Noninvasive technologies measure the alteration of a probing or excitation signal, such as near-infrared radiation, emitted radiation from the body, and radio wave, by specific properties of tissue, such as absorption, scattering, impedance, optical rotation, and fluorescence. However, other sample constituents of tissue often interfere, and the specific response, (the alternation of the probing or excitation signal due to or related to glucose) is greatly attenuated or completely obscured.
For example, one may consider the measurement of glucose through near-infrared spectroscopy on the basis of the absorption of glucose. In a near-infrared absorption spectrum, a change in the concentration of glucose is reflected by a change in the absorption of light according to the absorption and scattering properties of glucose and/or the effect of glucose changes upon the anatomy and physiology of the sampled site. However, in addition to the effect of glucose on the near-infrared light probing signal that is delivered to the skin, the probing signal is also reflected, diffusely reflected, transmitted, scattered, and absorbed in a complex manner related to the structure and composition of the tissue. When near-infrared light is delivered to the skin, a percentage of it is reflected, while the remainder penetrates into the skin. The proportion of reflected light, or specular reflectance, is typically between 4–7% of the delivered light over the entire spectrum from 250–3000 nm for a perpendicular angle of incidence. J. Parrish, R. Anderson, F. Urbach, D. Pitts, UV-A: Biologic Effects of Ultraviolet Radiation with Emphasis on Human Responses to Longwave Ultraviolet, New York, Plenum Press (1978). The 93–96% of the incident light that enters the skin is attenuated due to absorption and scattering within many layers of the skin. These two processes, combined with the orientation of the spectrometer sensors, determine the tissue volume irradiated by the source and “sampled” through the collection of diffusely reflected light.
Diffuse reflectance or remittance is defined as that fraction of incident optical radiation that is returned from a turbid sample as a function of wavelength. Alternately, diffuse transmittance is the fraction of incident optical radiation that is transmitted through a turbid sample. Absorption by the various skin constituents mentioned above accounts for the spectral extinction of the light within each layer. Scattering is the main process by which the beam may be returned to contribute to the diffuse reflectance of the skin. Scattering also has a strong influence on the light that is diffusely transmitted through a portion of the skin.
The scattering of light in tissues is in part due to discontinuities in the refractive indices on the microscopic level, such as the aqueous-lipid membrane interfaces between each tissue compartment or the collagen fibrils within the extracellular matrix. B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, 26:12 (December 1990). The spatial distribution and intensity of scattered light depends upon the size and shape of the particles relative to the wavelength, and upon the difference in refractive index between the medium and the constituent particles. The scattering of the dermis is dominated by the scattering from collagen fiber bundles in the 2.8 μm diameter range occupying twenty-one percent of the dermal volume, and the refractive index mismatch is 1.38/1.35 S. Jacques, Origins of tissue optical properties in the UVA, Visible and NIR Regions, Optical Society of America, Topical Meeting, Orlando Fla. (Mar. 18–22, 1996). The spectral characteristics of diffuse remittance from tissue result from a complex interplay of the intrinsic absorption and scattering properties of the tissue, the distribution of the heterogeneous scattering components, and the geometry of the point(s) of irradiation relative to the point(s) of light detection.
The near-infrared absorption of light in tissue is primarily due to overtone and combination absorbances of C—H, N—H, and O—H functional groups. As skin is primarily composed of water, protein, and fat; these functional groups dominate the near-IR absorption in tissue. As the main constituent, water dominates the near-infrared absorbance above 1100 nm and is observed through pronounced absorbance bands at 1450, 1900, and 2600 nm. Protein in its various forms, in particular, collagen is a strong absorber of light that irradiates the dermis. Near-infrared light that penetrates to subcutaneous tissue is absorbed primarily by fat. In the absence of scattering, the absorbance of near-infrared light due to a particular analyte, A, can be approximated by Beer's Law at each wavelength by:A=εcl  (1)where a is the analyte specific absorption coefficient, c is the concentration and l is the pathlength. An approximation of the overall absorbance at a particular wavelength is the sum of the individual absorbance of each particular analyte given by Beer's Law. The concentration of a particular analyte, such as glucose, can be determined through a multivariate analysis of the absorbance over a multiplicity of wavelengths because a is unique for each analyte. However, in tissue compartments expected to contain glucose, the concentration of glucose is at least three orders of magnitude less than that of water. Given the known extinction coefficients of water and glucose, the signal targeted for detection by reported approaches to near-infrared measurement of glucose, i.e. the absorbance due to glucose in the tissue, is expected to be, at most, three orders of magnitude less than other interfering tissue constituents. Therefore, the near-infrared measurement of glucose requires a high level of sensitivity over a broad wavelength range. Multivariate analysis is often utilized to enhance sensitivity.
In addition, the diverse scattering characteristics of the skin, e.g. multiple layers and heterogeneity, cause the light returning from an irradiated sample to vary in a highly nonlinear manner with respect to tissue analytes, in particular, glucose. Simple linear models, such as Beer's Law have been reported to be invalid for the dermis. R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, 77:1, pp. 13–19 (1981). Such nonlinear variation is a recognized problem and several reports have disclosed unique methods for compensating for the nonlinearity of the measurement while providing the necessary sensitivity. S. Malin, et al., supra; E. Thomas, R. Rowe, Methods and apparatus for tailoring spectroscopic calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000).
Dynamic Properties of the Skin
While knowledge and utilization of skin properties, high instrument sensitivity, and compensation for inherent non-linearities are all vital to the application of noninvasive technologies in blood analyte measurement, an understanding of the biological and chemical mechanisms that lead to time dependent changes in the properties of skin tissue is equally important and yet, largely ignored. At a given measurement site, skin tissue is often assumed to remain static, except for changes in the target analyte and other interfering species. However, variations in the physiological state and fluid distribution of tissue profoundly affect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations are often dominated by fluid compartment equalization through water shifts and are related to hydration levels and changes in blood analyte levels. A. Guyton, J. Hall, Textbook of Medical of Physiology, 9th ed., Philadelphia, W.B. Saunders Co. (1996).
Problem Statement and Description of Related Technology
A major difficulty in the noninvasive measurement of biological constituents and analytes in tissue through near-infrared spectroscopy arises from the fact that many constituents, such as glucose, are present in very small concentrations compared to sources of interference. In particular, the complex, heterogeneous and dynamic composition of the skin, together with profound variation over time, between tissue sample sites within a patient and from patient-to-patient interferes with and thereby attenuates the net analyte signal of many target analytes, such as glucose. In addition, the actual tissue volume sampled and the effective or average pathlength of light are varied. Therefore, the optical properties of the tissue sample are modified in a highly nonlinear and profound manner that introduces significant interference into noninvasive tissue measurements. Both calibration and measurement using noninvasive measurement devices would benefit from a method that attenuates the components of spectral interference related to the heterogeneity of the tissue, patient-to-patient differences, and variation through time (e.g., physiological effects).
Several methods are reported to compensate in some part for the dynamic variation of the tissue and patient-to-patient differences. For example, noninvasive measurement of glucose through calibration models that are specific to an individual over a short period of time are reported. K. H. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa (August 1995); J. J. Burmeister, In vitro model for human noninvasive blood glucose measurements,” Doctoral Dissertation, University of Iowa (December 1997).
This approach avoids modeling the differences between patients and therefore cannot be generalized to more individuals. In addition, the calibration models have not been tested over long time periods and do not provide a means for correcting for variation related to sample sites or physiological effects.
Several other approaches exist that employ diverse preprocessing methods to remove spectral variation related to the sample and instrumental variation including multiplicative signal correction (P. Geladi, D. McDougall and H. Martens, Applied Spectroscopy, vol. 39, pp. 491–500, 1985), standard normal variate transformation (R. J. Barnes, M. S. Dhanoa, and S. Lister, Applied Spectroscopy, 43, pp. 772–777, 1989), piecewise multiplicative scatter correction (T. Isaksson and B. R. Kowalski, Applied Spectroscopy, 47, pp. 702–709, 1993), extended multiplicative signal correction (H. Martens and E. Stark, J. Pharm Biomed Anal, 9, pp. 625–635, 1991), pathlength correction with chemical modeling and optimized scaling (T. Isaksson, Z. Wang, and B. R. Kowalski, J. Near Infrared Spectroscopy, 1, pp. 85–97, 1993), and FIR filtering (S. T. Sum, Spectral Signal Correction for Multivariate Calibration, Doctoral Dissertation, University of Delaware, 1998). In addition, a diversity of signal, data, or pre-processing techniques are commonly reported with the fundamental goal of enhancing accessibility of the net analyte signal. D. L. Massart, B. G. M. Vandeginste, S. N. Deming, Y. Michotte and L. Kaufman, Chemometrics: a textbook, Elsevier Science Publishing Company, Inc., pp. 215–252,1990; A. V. Oppenheim and R. W. Schafer, Digital Signal Processing, Englewood Cliffs, Prentice Hall, 1975, pp. 195–271; M. Otto, Chemometrics, Weinheim: Wiley-VCH, 1999; and K. R. Beebe., R. J. Pell and M. B. Seasholtz, Chemometrics A Practical Guide, John Wiley & Sons, Inc., pp. 26–55, 1998. Notably, Sum describes a solution to variation due to changes in a given physical sample and instrumental effects through the use of signal preprocessing techniques. The reported method reduces the variance in the spectral measurement arising from non-chemical sources while retaining the variance caused by chemical change. The sources of variance include the physical traits of the sample(s), such as, particle size and shape, packing density, heterogeneity, and surface roughness. The method includes preprocessing through a derivative step (see A. Savitzky and M. J. E. Golay. Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Anal. Chem., vol. 36, no. 8, pp. 1627–1639, 1964) followed by a spectral transformation through either multiplicative scatter correction or standard normal variate transformation. In addition, a FIR filter is described which for certain applications is found to be more effective in reducing both the instrumental and sample related variation.
While methods for preprocessing effectively compensate for variation related to instrument and physical changes in the sample and enhance the net analyte signal in the presence of noise and interference, they are inadequate for compensating for the sources of tissue related variation defined above. For example, the highly nonlinear effects related to sampling different tissue locations cannot be effectively compensated for through a pathlength correction because the sample is multi-layered, heterogeneous, and leads to large nonlinear variation. In addition, fundamental assumptions inherent in these methods, such as the constancy of multiplicative and additive effects across the spectral range and homoscadasticity of noise are violated in the noninvasive tissue application.
E. V. Thomas and R. K. Rowe have disclosed a method for reducing intra-subject variation through the process of mean-centering both the direct and indirect measurements for calibration and prediction. E. V. Thomas and R. K. Rowe, Methods and Apparatus for Tailoring Spectroscopic Calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000). However, that patent does not address the key problem related to sample heterogeneity and complexity, physiological and chemical variation related to the dynamic nature of the tissue, and the common problem of optical variation that occurs from sample-to-sample. In addition, the method is applied to the raw spectroscopic measurement and, as a result, it is dominated by variation resulting from surface effects such as surface roughness, hydration, coupling efficiency, and reflectance.
In view of the problems left unsolved by the prior art, there exists a need for a method and apparatus to reduce interference in tissue measurements related sample heterogeneity, time related variations, patient-to-patient differences, and instrumental effects.